Modeling

Data-based Storytelling

Daniel Winkler

Institute for Retailing & Data Science

Stephan Fally

Department Marketing

Getting into modelling!

Required skills

Data-science teams have different skill requirements:

Types of analyses

Four types of data stories:
Descriptive - What has happened?
Predictive - What will happen?
Causal - Why does it happen?
Prescriptive - What actions should be taken?

Setup: `ggplot2`

Import required packages
Define a list of layers for consistent styling throughout the project

library(ggplot2)
library(scales)
time_series_ggstyle <- list(
  scale_y_continuous(labels = label_number(scale_cut = cut_si(' ')), expand = c(0, 0.1)),
  theme_bw(base_size = 20),
  theme(
    axis.title.x = element_blank(),
    panel.border = element_blank(),
    axis.line = element_line(color = "black"),
    legend.position = 'top'
  )
)

Descriptive

Code

library(readr)
library(stringr)
library(tidyverse)
library(data.table)
options(scipen = 99999)
charts <- fread("data/charts_at_global.csv.gz")
ts <- str_detect(tolower(charts$artistName), "taylor swift")
charts_ts <- charts[ts, ]
filter(charts_ts, format(day, "%Y") == "2019" & region == "global") |>
  group_by(day) |>
  mutate(streams = sum(streams)) |>
  ggplot(aes(x = day, y = streams)) +
  geom_line() +
  scale_x_date(
    breaks = seq(as.Date("2019-01-01"), as.Date("2019-12-31"), "month"),
    date_labels = "%b"
  ) +
  geom_vline(xintercept = as.Date("2019-08-23"), color = "red") +
  annotate("text", x = as.Date("2019-08-20"), label = "Release of 'Lover'", y = 40000000, colour = "red", angle = 90, size = 8) +
  ggtitle("Taylor Swift Streams", subtitle = "Songs in top 200 - 2019") +
  time_series_ggstyle

Predictive

Code

library(zoo)
library(prophet)
total_streams <- charts |>
  filter(region == "global") |>
  group_by(day) |>
  summarize(y = sum(streams)) |>
  mutate(ds = as.Date(day)) |>
  select(-day)
total_streams_model <- filter(total_streams, ds <= as.Date("2020-12-31"), ds >= as.Date("2019-01-01")) 
total_streams_holdout <- filter(total_streams, ds >= as.Date("2021-01-01"))
mod <- prophet(total_streams_model,
               holidays = data.frame(
                 holiday = "christmas",
                 ds = c(
                    as.Date("2019-12-25"), 
                    as.Date("2020-12-25"), 
                    as.Date("2021-12-25")),
                 lower_window = -1, upper_window = 0
               ),
               daily.seasonality = FALSE
)
future <- make_future_dataframe(mod, periods = 365)
forecast <- predict(mod, future)
plot(mod, forecast) +
  labs(
    y = "Streams",
    title = "Prediction of total global streams of top 200",
    subtitle = "Observed: 2019-2020, forecast: 2021 (holdout: red)"
  ) +
  time_series_ggstyle +
  geom_point(data = total_streams_holdout, 
  aes(x = as.POSIXct(ds), y = y), color = 'red')

What about Causality?

Causal inference and Prediction

Variables can be predictive without a causal relationship
Correlation does not imply causation
Arcade revenue predicts CS doctorates (and vice versa)

Variables can be bad predictors but have a causal relationship
No correlation does not imply no causation
Fuel used and speed on cruise control (uphill vs. flat)

Variables can be predictive while not being predictive

Correlation does not even imply correlation

Andrew Gelman

Selection bias

For which “population” is the sample representative?

Causal but no correlation

Code

set.seed(123)
xy <- data.frame(x = rnorm(100000))
xy$y <- 0.5 * xy$x^2 + 2 * xy$x^4
ggplot(xy, aes(x = x, y = y)) +
geom_line() +
geom_smooth(method = "lm", color = "blue") +
labs(title = expression(y == 0.5 * x^2 + 2 * x^4), subtitle = "Non-linear relation") +
annotate("text",
x = -1, y = 25,
label = paste0(
"Best linear fit. Correlation: ",
round(cor(xy$x, xy$y), 3)
), hjust = 0, color = "blue", size =8
) +
time_series_ggstyle

Correlation without Correlation

Code

set.seed(42)
xy <- data.frame(x = rnorm(1000), y = rnorm(1000))
xy$obs <- abs(xy$x + xy$y) < 0.5 + runif(1000,0,2)
ggplot(xy, aes(x=x, y=y)) +
    geom_point(aes(color=obs)) +
    geom_smooth(data = xy[xy$obs,], 
    method = 'lm', se = FALSE, color = "#00BFC4") +
    geom_smooth(method = 'lm', se = FALSE) +
    time_series_ggstyle +
    labs(color = "Observed",
        title = "Restaurant and location quality", 
        subtitle="Survivor bias",
        y = "Restaurant Quality", x = "Location Quality") +
    annotate('text', 
            x = 2, y = -0.2, hjust=0,
            label = "Population regression line",
            color = "blue", size = 8 ) +
    theme(axis.title.x = element_text())

Correlation but different but still Correlation

Equivalent datasets based on estimate

Code

library(datasauRus)
library(kableExtra)
suppressPackageStartupMessages(library(dplyr))
data <- datasaurus_dozen %>%
filter(dataset %in% c(
"away",
"bullseye",
"circle",
"dino",
"high_lines",
"wide_lines",
"x_shape",
"star"
))
data %>%
group_by(dataset) %>%
summarize(
mean_x    = round(mean(x), 2),
mean_y    = round(mean(y), 2),
std_dev_x = round(sd(x), 2),
std_dev_y = round(sd(y), 2),
corr_x_y  = round(cor(x, y), 2)
) %>%
mutate(dataset = stringr::str_replace(dataset, "_", " ")) %>%
kbl(
col.names =
c("data", "mean x", "mean y", "sd x", "sd y", "corr x,y"),
format = "html", table.attr = "style='width:100%;'"
) %>%
column_spec(1, width = "3cm")

data	mean x	mean y	sd x	sd y	corr x,y
away	54.27	47.83	16.77	26.94	-0.06
bullseye	54.27	47.83	16.77	26.94	-0.07
circle	54.27	47.84	16.76	26.93	-0.07
dino	54.26	47.83	16.77	26.94	-0.06
high lines	54.27	47.84	16.77	26.94	-0.07
star	54.27	47.84	16.77	26.93	-0.06
wide lines	54.27	47.83	16.77	26.94	-0.07
x shape	54.26	47.84	16.77	26.93	-0.07

Always visualize

Code

library(ggplot2)
library(colorspace)
ggplot(data, aes(x = x, y = y, colour = dataset)) +
geom_point(size = 4.5) +
theme_void() +
theme(
legend.position = "none",
strip.text.x = element_text(size = 30)
) +
facet_wrap(~dataset, nrow = 4) +
scale_color_discrete_qualitative(palette = "Dynamic")

Quality of music and income

Data: Ratings by music experts, genre, streams
Question: Causal effect of rating on income

Code

library(modelsummary)
library(gt)
set.seed(1)
N <- 5000
genre <- rbinom(N, 1, 0.5)
perfect_rating <- as.factor(rbinom(N, 5, 0.8 - 0.7 * genre)>4)
streams <- rexp(N, 0.01 - 0.003 * genre) |> floor()
modelsummary(
    list(lm(streams~perfect_rating),
    lm(streams~genre),
    lm(streams~perfect_rating + genre)),
    coef_rename = c("perfect_ratingTRUE" = "perfect rating"),
    stars = TRUE,
    statistic = "{p.value}",
    gof_map = NA)

	(1)	(2)	(3)
(Intercept)	122.711***	97.408***	97.314***
	<0.001	<0.001	<0.001
perfect rating	−25.112***		0.285
	<0.001		0.956
genre		43.268***	43.361***
		<0.001	<0.001
+ p < 0.1, * p < 0.05, p < 0.01, * p < 0.001

Grades and happyness

Data: grades, self_esteem index, happyness index
Question: Causal effect of grades on happyness

Code

set.seed(42)
range_normalize <- function(x, min_range = 0, max_range = 100){
  x_norm <- min_range + (x - min(x)) * (max_range - min_range) / (max(x) - min(x))
}
grades <- 1 + rbinom(N, 4, 0.04)
self_esteem <- range_normalize(100 * (1/grades + rnorm(N)))
happyness <- range_normalize(5 + self_esteem + rnorm(N))
modelsummary(
    list(lm(happyness~grades),
    lm(happyness~self_esteem),
    lm(happyness~grades+self_esteem)),
    coef_rename = c("self_esteem" = "self esteem"),
    stars = TRUE,
    statistic = "{p.value}",
    gof_map = NA)

	(1)	(2)	(3)
(Intercept)	61.393***	1.510***	1.551***
	<0.001	<0.001	<0.001
grades	−5.958***		−0.029
	<0.001		0.430
self esteem		0.989***	0.988***
		<0.001	<0.001
+ p < 0.1, * p < 0.05, p < 0.01, * p < 0.001

Restaurant and location ratings

Data: Restaurant ratings, location ratings, restaurant survival prob.
Question: Causal effect of location rating on restaurant rating

Code

set.seed(14)
restaurant_rating <- 25 + 10*rnorm(N)
location_rating <- 5*rnorm(N)
survival_probability <- range_normalize(0.8*restaurant_rating + 0.8 * location_rating + 10*rnorm(N), 0, 100)
modelsummary(
    list(lm(restaurant_rating~location_rating),
    lm(restaurant_rating~survival_probability),
    lm(restaurant_rating~survival_probability + location_rating)),
    coef_rename = c(
                    "location_rating" = "location rating",
                    "survival_probability" = "survival prob."
                    ),
    stars = TRUE,
    output = 'gt',
    statistic = "{p.value}",
    gof_map = NA) |>
    tab_style(style = cell_fill(color='lavenderblush3'),
              locations = cells_body(rows = 3)
    )

	(1)	(2)	(3)
(Intercept)	24.875***	3.151***	0.996*
	<0.001	<0.001	0.023
location rating	0.022		-0.390***
	0.440		<0.001
survival prob.		0.414***	0.455***
		<0.001	<0.001
+ p < 0.1, * p < 0.05, p < 0.01, * p < 0.001

Causal Inference: two approaches (Imbens 2020)

Directed Acyclic Graphs (DAGs)
Concerned with identification of causal relationships
Shows direction of causality and important variables
Graphical representation:

Code

library(ggdag)
library(dagitty)
library(tidyverse)
dagify(y ~ x, x ~ z, exposure = "x", outcome = "y",
       coords = list(x = c(x = 1, y = 1.5, z = 1), y = c(x=1, y = 1, z=0))
) %>% 
  tidy_dagitty() %>%
  ggdag(text_size = 8, node_size = 10) +
  geom_dag_edges() +
  annotate("text", x = 1.2, y = 1, vjust=1, label= "x causes y", size=5 ) + 
  annotate("text", x = 1, y = 0.5, hjust=-0.1, label="z causes x", size = 5) +
  theme_dag()

Potential Outcome
Multiple Treatments / Causes
e.g., exposure to ad
Potential outcomes f. treatments
e.g., Purchase given exposure / no exposure
Multiple observations with different treatments
e.g., A/B test
Focus on assignment of treatment
e.g., randomized experiment, selection on (un)observables

\[ \begin{aligned} y_i(0)& \ldots \text{outcome of individual }i\text{ without treatment} \\ y_i(1)& \ldots \text{outcome of individual }i\text{ with treatment} \\ \delta_i = y&_i(1) - y_i(0) \ldots \text{treatment effect of individual }i \end{aligned} \]

Observed: \(y_i = D_i * y_i(1) + (1-D_i) * y_i(0)\) where \(D_i\) is the treatment indicator

Causal Pitchfork

Analyzing DAGs: d-separation

Necessary to decide which variables to use in model
“d” stands for “directional”
Usually we are dealing with more than two variables
Complication: causation flows only directed - association might flow against

Code

dagify(z ~ x, y2 ~ z, a ~ x, a ~ y3, x ~ d, y1 ~ d,
       coords = list(x = c(x = 1, z = 1.5, y2 = 2, a = 1.5, y3 = 2, d = 1.5, y1 = 2), 
                     y = c(x = 1, y2 = 1, z = 1, a = 0, y3 = 0, d = 2, y1 = 2))
) %>% 
  tidy_dagitty() %>%
  ggdag(text_size = 3, node_size = 5) +
  geom_dag_edges() +
  theme_dag() +
  labs(title= "Causal Pitchfork", subtitle = "x and y2 are d-connected but x and y1/y3 are not") +
  theme(title = element_text(size = 8))

Analyzing DAGs: Fork

Good Control

Code

med <- dagify( x ~ d, y1 ~ d,
       coords = list(x = c(x = 1, z = 1.5, y = 2, a = 1.5, b = 2, d = 1.5, y1 = 2), 
                     y = c(x = 1, y = 1, z = 1, a = 0, b = 0, d = 2, y1 = 2))
) %>% 
  tidy_dagitty() %>%
  mutate(fill = ifelse(name == "d", "Confounder", "variables of interest")) %>% 
  ggplot(aes(x = x, y = y, xend = xend, yend = yend)) + 
  geom_dag_point(size=7, aes(color = fill)) + 
  geom_dag_edges(show.legend = FALSE)+
  geom_dag_text() +
  theme_dag() +
  theme(legend.title  = element_blank(),
        legend.position = "top") 
med

d causes both x and y1
Arrows pointing to x are called “back-door” paths
Eliminated by randomized experiment! Why?
Controlling for d “blocks” the non-causal association x \(\rightarrow\) y1

Analyzing DAGs: Pipe

Bad Control (possibly use mediation analysis)

Code

med <- dagify(z ~ x, y2 ~ z,
       coords = list(x = c(x = 1, z = 1.5, y2 = 2), y = c(x=1, y2 = 1, z=1))
) %>% 
  tidy_dagitty() %>%
  mutate(fill = ifelse(name == "z", "Mediator", "variables of interest")) %>% 
  ggplot(aes(x = x, y = y, xend = xend, yend = yend)) + 
  geom_dag_point(size=7, aes(color = fill)) + 
  geom_dag_edges(show.legend = FALSE)+
  geom_dag_text() +
  theme_dag() +
  theme(legend.title  = element_blank(),
        legend.position = "top") 
med

x causes y through z
Controlling for z blocks the causal association x \(\rightarrow\) y2

Analyzing DAGs: Collider

Bad control

Code

dagify(a ~ x, a ~ y,
  coords = list(x = c(x = 1, y = 2, a = 1.5), y = c(x = 1, y = 0,  a = 0))
) |>
  tidy_dagitty() |>
  mutate(fill = ifelse(name == "a", "Collider", "variables of interest")) |>
  ggplot(aes(x = x, y = y, xend = xend, yend = yend)) +
  geom_dag_point(size = 7, aes(color = fill)) +
  geom_dag_edges(show.legend = FALSE) +
  geom_dag_text() +
  theme_dag() +
  theme(
    legend.title = element_blank(),
    legend.position = "top"
  )

x & y cause a
x & y are d-separated and uncorrelated
By adding a to the model spurious correlation between x & y is introduced

Exercise

Which variables should be included?

Effect of x on y
Effect of z on y

Code

library(ggdag)
library(dagitty)
library(tidyverse)
dagify(y ~ n + z + b + c, 
       x ~  z + a + c,
       n ~ x,
       z ~ a + b, exposure = "x", outcome = "y",
       coords = list(x = c(n = 2, x = 1, y = 3, a = 1, z = 2, c = 2, b = 3), y = c(x = 2, y = 2, a = 3, z = 3, c = 1, b = 3, n = 2))) %>% 
  tidy_dagitty() %>%
  ggdag(text_size = 8, node_size = 12) +
  geom_dag_edges() +
  theme_dag()

Common bad controls (Cinelli, Forney, and Pearl 2020)

Code

library(ggpubr)
p1 <- dagify(y ~ x + U2,
       a ~ U1 + U2,
       x ~ U1,
      coords = list(x = c(x = 1, y = 2, a = 1.5, b = 1.5, U1 = 1, U2 = 2), y = c(x=1, y = 1,  a = 1.5, b = 0, U1 = 2, U2 = 2))
) %>% 
  tidy_dagitty() %>%
  mutate(fill = ifelse(name %in% c("U1", "U2"), "Unobserved", "Observed")) %>% 
  ggplot(aes(x = x, y = y, xend = xend, yend = yend)) + 
  geom_dag_point(size=12, 
                 aes(color = fill)
                 ) + 
  geom_dag_edges(show.legend = FALSE)+
  geom_dag_text() +
  theme_dag() +
  theme(legend.title  = element_blank(),
        legend.position = "bottom") +
  labs(title = "M-Bias")
p2 <- dagify(y ~ a + U,
       a ~ x + U,
      coords = list(x = c(x = 1, y = 2, a = 1.5, b = 1.5, U = 1.7, U2 = 2), y = c(x=1, y = 1,  a = 1, b = 0, U = 2, U2 = 2))
) %>% 
  tidy_dagitty() %>%
  mutate(fill = ifelse(name %in% c("U"), "Unobserved", "Observed")) %>% 
  ggplot(aes(x = x, y = y, xend = xend, yend = yend)) + 
  geom_dag_point(size=12, 
                 aes(color = fill)
                 ) + 
  geom_dag_edges(show.legend = FALSE)+
  geom_dag_text() +
  theme_dag() +
  theme(legend.title  = element_blank(),
        legend.position = "bottom") +
  labs(title = "Post-treatment Bias")
ggarrange(p1, p2)

Common bad controls

Code

p1 <- dagify(y ~ x ,
       a ~ x + y,
      coords = list(x = c(x = 1, y = 2, a = 1.5, b = 1.5, U1 = 1, U2 = 2), y = c(x=1, y = 1,  a = 1.5, b = 0, U1 = 2, U2 = 2))
) %>% 
  tidy_dagitty() %>%
  #mutate(fill = ifelse(name %in% c("U1", "U2"), "Unobserved", "Observed")) %>% 
  ggplot(aes(x = x, y = y, xend = xend, yend = yend)) + 
  geom_dag_point(size=12, 
                 #aes(color = fill)
                 ) + 
  geom_dag_edges(show.legend = FALSE)+
  geom_dag_text() +
  theme_dag() +
  theme(legend.title  = element_blank(),
        legend.position = "bottom") +
  labs(title = "Selection Bias")
p2 <-  dagify(y ~ x ,
       a ~ y,
      coords = list(x = c(x = 1, y = 2, a = 1.5, b = 1.5, U1 = 1, U2 = 2), y = c(x=1, y = 1,  a = 1.5, b = 0, U1 = 2, U2 = 2))
) %>% 
  tidy_dagitty() %>%
  #mutate(fill = ifelse(name %in% c("U1", "U2"), "Unobserved", "Observed")) %>% 
  ggplot(aes(x = x, y = y, xend = xend, yend = yend)) + 
  geom_dag_point(size=12, 
                 #aes(color = fill)
                 ) + 
  geom_dag_edges(show.legend = FALSE)+
  geom_dag_text() +
  theme_dag() +
  theme(legend.title  = element_blank(),
        legend.position = "bottom") +
  labs(title = "Case-control Bias")
ggarrange(p1, p2)

Intelligence, education, income

Case-control study: Observation ex-post. Ex.: Smoking \(\rightarrow\) lung cancer

Exercise

Prepare a short presentation of a (potential) DAG for your thesis

References

Cinelli, Carlos, Andrew Forney, and Judea Pearl. 2020. “A Crash Course in Good and Bad Controls.” SSRN 3689437.

Imbens, Guido W. 2020. “Potential Outcome and Directed Acyclic Graph Approaches to Causality: Relevance for Empirical Practice in Economics.” Journal of Economic Literature 58 (4): 1129–79. https://doi.org/10.1257/jel.20191597.

Modeling

Getting into modelling!

Required skills

Types of analyses

Setup: ggplot2

Descriptive

Predictive

What about Causality?

Causal inference and Prediction

Selection bias

Causal but no correlation

Correlation without Correlation

Correlation but different but still Correlation

Quality of music and income

Grades and happyness

Restaurant and location ratings

Causal Inference: two approaches (Imbens 2020)

Analyzing DAGs: d-separation

Analyzing DAGs: Fork

Analyzing DAGs: Pipe

Analyzing DAGs: Collider

Exercise

Common bad controls (Cinelli, Forney, and Pearl 2020)

Common bad controls

Exercise

References

Setup: `ggplot2`